Use of freely available datasets and machine learning methods in predicting deforestation

The range and quality of freely available geo-referenced datasets is increasing. We evaluate the usefulness of free datasets for deforestation prediction by comparing generalised linear models and generalised linear mixed models (GLMMs) with a variety of machine learning models (Bayesian networks, artificial neural networks and Gaussian processes) across two study regions. Freely available datasets were able to generate plausible risk maps of deforestation using all techniques for study zones in both Mexico and Madagascar. Artificial neural networks outperformed GLMMs in the Madagascan (average AUC 0.83 vs 0.80), but not the Mexican study zone (average AUC 0.81 vs 0.89). In Mexico and Madagascar, Gaussian processes (average AUC 0.89, 0.85) and structured Bayesian networks (average AUC 0.88, 0.82) performed at least as well as GLMMs (average AUC 0.89, 0.80). Bayesian networks produced more stable results across different sampling methods. Gaussian processes performed well (average AUC 0.85) with fewer predictor variables

Modeling Deforestation and CO2 Emissions in Tropical Forests (Western South Amazon)

Spatial modeling is a tool to represent deforestation and predict future scenarios according to different landscape change. Establishing 80% Legal Reserve Area (LR) in the Amazon since 90th, the Brazilian forestry code has made clear the biodiversity conservation profile of the largest tropical forest in the world. However, this mechanism did not prevent the advance of deforestation, which in recent years has increased again. This remote tool aims to monitor the deforestation, simulating its possible future trajectories, and thus generate information that can be used to assist in the management of deforestation reduction. The spatial modeling in the prediction of different deforestation scenarios based on public policies and their changes to the state of Acre (north of Brazil). Using the methodological processes of the Dinamica EGO software, three scenarios were projected up to the year 2050: (1) deforestation “Business as usual”, (2) deforestation with 50% LR and (3) deforestation with 80% LR provided by law. Based on these results it was evident that maintaining and respect 80% LR, it’s possible reduce the CO2 emissions more than 76%, avoiding around 119,534,836 t of CO2 and influences positively on reducing deforestation. Dinamica EGO proved to be an effective to represent the deforestation

Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

Bayesian networks have become a popular modelling technique in many fields, however there are several design decisions that, if poorly made, can result in models with insufficient evidence to make good predictions. One such decision is how to discretise the continuous nodes. The lack of a commonly accepted algorithm for achieving this makes it a difficult task for novice data modellers. We present a structure aware discretisation algorithm that minimises the number of missing values in the conditional probability tables by taking into account the network structure. It also prevents users from having to specify the exact number of bins. Results from two water quality case studies in south-east Queensland showed that the algorithm has potential to improve the discretisation process over equal case discretisation and demonstrates the suitability of Bayesian networks for this field

Integrating GIS approaches with geographic profiling as a novel conservation tool

PhDGeographic profiling (GP) was originally developed to solve the problem of information overload when dealing with cases of serial crime. In criminology, the model uses spatial data relating to the locations of connected crimes to prioritise the search for the criminal’s anchor point (usually a home or workplace), and is extremely successful in this field. Previous work has shown how the same approach can be adapted to biological data, but to date the model has assumed a spatially homogenous landscape, and has made no attempt to integrate more complex spatial information (eg, altitude, land use). It is this issue that I address here. In addition, I show for the first time how the model can be applied to conservation data and – taking the model back to its origins in criminology – to wildlife crime. In Chapter 2, I use the Dirichlet Process Mixture (DPM) model of geographic profiling to locate sleep trees for tarsiers in dense jungle in Indonesia, using as input the locations at which calls were recorded, demonstrating how the model can be applied to locating the nests, dens or roosts of other elusive animals and potentially improving estimates of population size, with important implications for management of both species and habitats. In Chapter 3, I show how spatial information in the form of citizen science could be used to improve a study of invasive mink in the Hebrides. In Chapter 4, I turn to the issue of ‘commuter crime’ in a study of poaching in Savé Valley Conservancy (SVC) in Zimbabwe, in which although poaching occurs inside SVC the majority of poachers live outside, showing how the model can be adjusted to reflect a simple binary classification of the landscape (inside or outside SVC). Finally, in Chapter 5, I combine more complex land use information (estimates of farm density) with the GP model to improve predictions of human-wildlife conflict.National Environment Research Council and Queen Mary University of London

Dinámica de la Ocupación del Suelo en la Cuenca del Río Combeima, Colombia (1991-2015)

Colombia es el segundo país más biodiverso en el mundo y está localizado en la zona intertropical. Allí sus cuencas hidrográficas presentan una variedad de coberturas y usos que están siendo sometidos a unas dinámicas que amenazan esta riqueza biológica, principalmente por acciones antrópicas. Este es el caso de la cuenca del rio Combeima, considerada un ecosistema estratégico por proveer servicios ambientales, tales como el suministro de agua para gran parte de la ciudad de Ibagué y para el riego de áreas claves en la producción de alimentos. Ante esto se han planteado dos objetivos que son complementarios: el primero, construir cartografía e indicadores espaciales y temporales de los cambios de la ocupación de la tierra en los periodos 1991-2005 y 2005-2015, que permitan dar respuesta a las siguientes inquietudes: ¿cuáles son las proporciones de los cambios espaciales y temporales ocurridos en la cobertura y usos de la tierra (CUT)?, ¿Qué categorías de CUT presentan cambios sistemáticos y en donde se ubican?. Y el segundo, seleccionar procedimientos para establecer los factores incidentes en la dinámica de la ocupación de la tierra. En su desarrollo se utilizan, de manera integrada, tecnologías de la información geográficas, técnicas de análisis estadístico explícitamente espaciales e interpretación de imágenes de satélite. Los resultados obtenidos revelan que la cuenca del río Combeima presenta cambios importantes en el sistema de uso de la tierra para el periodo 1991 y 2015. Se determinó la existencia de procesos de deforestación, crecimiento urbano, y recuperación/restauración, entre otros. Siendo el proceso de deforestación el más dinámico, presentando en el segundo periodo una tendencia a recuperarse el bosque. Mientras tanto el modelo de regresión utilizado identifica, del conjunto de variables analizadas, que la deforestación, entre 1991-2005 es condicionada principalmente por la densidad de población, la distancia al PNNN, la distancia a la zona urbana (1991) y la precipitación. La recuperación se da principalmente sobre áreas alejadas de la zona urbana y sobre terrenos de alta pendiente. Finalmente, el proceso de urbanización está especialmente incentivado por la densidad de población, la precipitación y el tamaño de los predios. De esta manera los resultados de la investigación serán insumo para integrarlo al plan de ordenación y manejo de la cuenca hidrográfica del río Combeima

Addressing uncertainty and limited data in conservation decision-making

PhD ThesisBiodiversity is declining worldwide at alarming rates, through a range of humaninduced changes. At the same time, there are great uncertainties and biases in our understanding of biodiversity that limit our ability to detect changes. New approaches in estimating and managing uncertainty can inform assessments of the status of biodiversity, and identify what actions might be most beneficial. The thesis examines the applications of these methods in diverse contexts that are of importance to conservation and in which there is limited data available. The potential for Value of Information method to contribute to the prioritisation of conservation action was explored (chapter 2). While its use is increasing, there are currently substantial gaps in its application. Probabilistic graphical models (Bayesian Networks) were built with different Machine Learning algorithms to predict the Red List status of plants, both in the Caatinga region in Brazil (chapter 3) and globally (chapter 4) and to assess why some tiger reserves contain higher tiger numbers than others (chapter 5). Red List status of plants could be predicted reliably by using the number of herbarium specimens of each plant species. The method was used to predict which plants might be threatened globally. The number of poached tigers was a good indicator for the number of tigers in a tiger reserve, but a lack of data at similar spatial scales across the tigers’ range inhibits decision making. Overall, the thesis suggests that we can: a) better predict which species are threatened and prioritise these species for future Red List assessments; b) standardise our research approaches using core outcomes; and c) make better decisions despite uncertainty. We need to make better use of these methods and the currently available data to prevent species from going extinct and to meet global targets aimed to halt the biodiversity crisis